Tailoring Multimedia Environments to Learner Cognitive Characteristics

2009 ◽  
pp. 221-245
Author(s):  
Slava Kalyuga
Keyword(s):  
Continuous Process ◽  
Instructional Systems ◽  
Learner Characteristics ◽  
Tutoring Systems ◽  
Cognitive Characteristics ◽  
Individual Learner ◽  
Reversal Effect ◽  
Instructional Content ◽  
Multimedia Environments ◽  
Specific Expertise

Personalized adaptive multimedia environments provide individual learners or learner groups with experience that is specifically tailored to them. To achieve effective personalization, a variety of information about the learner is required. Tailoring multimedia environments to individual learner cognitive characteristics is becoming a major means for achieving a true learner-centered experience for learners through their interaction with multiple content sources, presentation formats, and delivery means. Personalized multimedia environments are capable of realizing advanced learning and instruction strategies based on a continuous process of adaptation between the learners and instructional systems. This adaptation process could be accomplished through personalized interaction and adaptive presentation of content, learner feedback, adaptive navigation and search, and different adaptation methodologies. As was mentioned in earlier chapters of this book, a major instructional implication of the expertise reversal effect is the need to tailor dynamically instructional techniques and procedures, levels of instructional guidance to current levels of learner task-specific expertise. In online multimedia instructional systems, the levels of learner task-specific expertise change as students develop more experience in a specific task domain. Therefore, the tailoring process needs to be dynamic, i.e. consider learner levels of expertise in real time as they gradually change during the learning sessions. This Chapter describes general approaches to the design of adaptive learning environments from the perspective of tailoring learning procedures and techniques to individual cognitive characteristics of learners. Studies in aptitude-treatment interactions offered a possible approach to adaptive instruction. Intelligent tutoring systems and adaptive web-based hypermedia systems use learner models to tailor learning tasks and instructional content to individual learner characteristics. This approach accommodates learner characteristics (e.g., knowledge, interests, goals) into explicit learner models that guide adaptive procedures. On the other hand, advisement and adaptive guidance approaches realize a greater learner control over instruction and provide individualized prescriptive information in the form of recommended material and tasks based on learner past performance.

Techniques, Technologies and Patents Related to Intelligent Educational Systems

2011 ◽  
pp. 1-28
Author(s):  
Jim Prentzas ◽  
Ioannis Hatzilygeroudis
Keyword(s):  
Artificial Intelligence ◽  
Learning Systems ◽  
Future Research ◽  
Educational Systems ◽  
Learner Characteristics ◽  
Tutoring Systems ◽  
Artificial Intelligence Methods ◽  
E Learning ◽  
Future Research Directions ◽  
Intelligent Methods

E-learning systems play an increasingly important role in lifelong learning. Tailoring the learning process to individual needs is a key issue in such systems. Intelligent Educational Systems (IESs) are e-learning systems employing Artificial Intelligence methods to effectively adapt to learner characteristics. Main types of IESs are Intelligent Tutoring Systems (ITSs) and Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent methods. In this chapter, the authors present technologies and techniques used in the primary modules of IESs and survey corresponding patents. They present issues and problems involving specific IES modules as well as the overall IES. The authors discuss solutions offered for such issues by Artificial Intelligence methods and patents. They also discuss categorization aspects of patents related to IESs and briefly present the work described in some representative patents. Lastly, the authors outline future research directions regarding IESs.

scholarly journals Adaptive Learning by Using SCOs Metadata

10.28945/3138 ◽  
2007 ◽  
Author(s):  
Danijela Milosevic ◽  
Mirjana Brkovic ◽  
Matjaz Debevc ◽  
Radojka Krneta
Keyword(s):  
Learning Style ◽  
Adaptive Learning ◽  
Instructional Material ◽  
Learner Characteristics ◽  
Instructional Content ◽  
Lesson Content ◽  
Motivation Level ◽  
Student Information ◽  
Future Work ◽  
Educational Category

This paper presents an adaptation scenario for tailoring instructional content towards individual learner characteristics taking into consideration his/her learning style type and subject matter motivation level. Learning resources are organized through shareable content objects (SCOs) - a small digital chunks of knowledge, independent and self described pieces of instructional material delivered via Learning Management System (LMS). We use an ontology based student model for storing student information. The scenario of designing lesson content is presented as a cross section of learning style and motivation level, based on the learning object’s educational metadata. Adaptation is made through discovering those SCO’s whose educational category metadata implies that SCO is to be delivered for the learning style of user. Our future work will be to provide experiment and to test our proposed guidelines in order to get feedback on how learners see the adaptive learning environments tailored to their individual learning style and motivation characteristics.

Assessment of Task-Specific Expertise

2009 ◽  
pp. 81-100
Author(s):  
Slava Kalyuga
Keyword(s):  
Real Time ◽  
Adaptive Learning ◽  
Diagnostic Approach ◽  
Problem Solution ◽  
Problem Situation ◽  
Learning Session ◽  
Online Application ◽  
Aptitude Tests ◽  
Reversal Effect ◽  
Specific Expertise

Main implication of the expertise reversal effect is the need to tailor instructional techniques and procedures to changing levels of learner expertise in a specific task domain. In order to design adaptive procedures capable of tailoring instruction in real-time, it is necessary to have online measures of learner expertise. Such measures should be rapid enough to be used in real time. At the same time, they need to have sufficient diagnostic power to detect different levels of task-specific expertise. One of the previously mentioned reasons for low practical applicability of the results of studies in Aptitude-Treatment Interactions were inadequate aptitude measures. Most of the assessment methods used in those studies were psychometric instruments designed for selection purposes (e.g., large batteries of aptitude tests based on artificially simplified tasks administered mostly in laboratory conditions). Another suggested reason was unsuitability of those methods for dynamic, real-time applications while learners proceeded through a single learning session. This chapter describes a rapid diagnostic approach to the assessment of learner task-specific expertise that has been intentionally designed for rapid online application in adaptive learning environments. The method was developed using an analogy to experimental procedures applied in classical studies of chess expertise mentioned in Chapter I. In those studies, realistic board configurations were briefly presented for subsequent replications. With the described diagnostic approach, learners are briefly presented with a problem situation and required to indicate their first solution step in this problem situation or to rapidly verify suggested steps at various stages of a problem solution procedure.

Promoting Self-Regulated Learning Through Prompts

2009 ◽  
Vol 23 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Maria Bannert
Keyword(s):  
Process Analysis ◽  
Assessment Methods ◽  
Learning Activities ◽  
Future Research ◽  
Special Issue ◽  
Learner Characteristics ◽  
Individual Learner ◽  
Self Regulated Learning ◽  
Regulated Learning

In this contribution the four papers of this special issue on “Promoting Self-Regulated Learning Through Prompts” are discussed with the help of two crucial questions: What learning activities should be prompted and how should they be prompted? Overall, it is argued that future research has to conduct more in depth process analysis that incorporates multi-method assessment methods and to further account for individual learner characteristics. Prompting research, at present, needs more insights on how students actually deal with learning prompts.

scholarly journals The Expertise Reversal Effect

2009 ◽  
pp. 58-80 ◽  
Author(s):  
Slava Kalyuga
Keyword(s):  
Empirical Evidence ◽  
Prior Knowledge ◽  
Expertise Reversal Effect ◽  
Reversal Effect ◽  
Cognitive Studies ◽  
Instructional Outcomes ◽  
Cognitive Activities ◽  
Memory Resources ◽  
Specific Expertise

Cognitive studies of expertise that were reviewed in Chapter I indicated that prior knowledge is the most important 1earner characteristic that influences learning processes. Recently, it has been established that learning procedures and techniques that are beneficial for learners with low levels of prior knowledge may become relatively inefficient for more knowledgeable learners due to cognitive activities that consume additional working memory resources. This expertise reversal effect could be related to aptitude-treatment interactions (interactions between learning outcomes of different instructional treatments and student aptitudes) that were actively investigated in 1960-70s. The learner level of prior knowledge or level of expertise is the aptitude of interest in this case. The effect is explained by the cognitive overload that more knowledgeable learners may experience due to processing redundant for these learners instructional components (as compared to information without redundancy). As a consequence, instructional outcomes of different multimedia learning formats and procedures are always relative to levels of learner task-specific expertise. This chapter describes cognitive processes that cause expertise reversal effect and major instructional implications of this effect. The chapter provides a review of empirical evidence obtained in the original longitudinal studies of the effect, the expertise reversal for methods of enhancing essential cognitive load, and expertise reversal phenomena when learning from textual and hypertextual materials. The chapter also describes relations between the expertise reversal effect and studies of Aptitude-Treatment Interactions. Additional empirical evidence for the effect in other areas will be described in the following chapters in Section 2 of the book.

Spatial and Nonspatial Integration in Learning and Training with Multimedia Systems

2009 ◽  
pp. 108-133 ◽  
Author(s):  
Tad T. Brunyé ◽  
Tali Ditman ◽  
Jason S. Augustyn
Keyword(s):  
Multimedia Systems ◽  
Pedagogical Practices ◽  
Future Research ◽  
Instructional Systems ◽  
Cognitive Mechanisms ◽  
Effective Learning ◽  
Research Areas ◽  
Instructional Content ◽  
And Training

Multiformat and modality interfaces have become popular and effective tools for presenting information in training and instructional systems. Technological innovation, however, has far surpassed researchers’ understanding of how and under what circumstances these technologies are useful towards information gathering. Some recent research has begun to characterize the cognitive mechanisms that may be responsible for the comprehension and memory advantages typically seen with multimedia learning, as well as the role of individual differences in this process. Other work has defined effective pedagogical practices, such as instructional content and organization, for producing engaging and effective learning experiences. This chapter attempts to bridge these two research areas and provides concrete design recommendations for current instructional practice and directions for future research.

Intelligent Tutoring Systems for Distributed Learning

2011 ◽  
pp. 162-183 ◽  
Author(s):  
Mohamed Ally
Keyword(s):  
Research And Development ◽  
Multidisciplinary Team ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Distributed Learning ◽  
Future Research ◽  
Student Model ◽  
Tutoring Systems ◽  
Individual Learner ◽  
Teaching Situation

This chapter provides information on how to design intelligent tutoring systems for distributed learning to cater to individual learner needs and styles. It argues that intelligent tutoring systems must use the expertise that tutors use in a one-to-one teaching situation to build intelligent tutoring systems for distributed learning. Also, the appropriate psychological and educational theories must be used to build the domain module, student model, and pedagogical module. The components of intelligent tutoring systems are described, and the author makes the case that to build effective intelligent tutoring systems, a multidisciplinary team should be involved. Finally, the author identifies trends that are influencing the development of intelligent tutoring systems and suggests areas for future research and development.

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